67 lines
2.4 KiB
Python
67 lines
2.4 KiB
Python
import logging
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import numpy as np
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from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
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from freqtrade.freqai.RL.Base4ActionRLEnv import Actions, Base4ActionRLEnv, Positions
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logger = logging.getLogger(__name__)
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class ReinforcementLearner_test_4ac(ReinforcementLearner):
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"""
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User created Reinforcement Learning Model prediction model.
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"""
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class MyRLEnv(Base4ActionRLEnv):
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"""
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User can override any function in BaseRLEnv and gym.Env. Here the user
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sets a custom reward based on profit and trade duration.
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"""
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def calculate_reward(self, action: int) -> float:
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# first, penalize if the action is not valid
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if not self._is_valid(action):
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return -2
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pnl = self.get_unrealized_profit()
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rew = np.sign(pnl) * (pnl + 1)
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factor = 100.
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# reward agent for entering trades
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if (action in (Actions.Long_enter.value, Actions.Short_enter.value)
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and self._position == Positions.Neutral):
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return 25
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# discourage agent from not entering trades
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if action == Actions.Neutral.value and self._position == Positions.Neutral:
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return -1
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max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
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trade_duration = self._current_tick - self._last_trade_tick # type: ignore
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if trade_duration <= max_trade_duration:
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factor *= 1.5
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elif trade_duration > max_trade_duration:
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factor *= 0.5
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# discourage sitting in position
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if (self._position in (Positions.Short, Positions.Long) and
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action == Actions.Neutral.value):
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return -1 * trade_duration / max_trade_duration
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# close long
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if action == Actions.Exit.value and self._position == Positions.Long:
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if pnl > self.profit_aim * self.rr:
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factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
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return float(rew * factor)
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# close short
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if action == Actions.Exit.value and self._position == Positions.Short:
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if pnl > self.profit_aim * self.rr:
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factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
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return float(rew * factor)
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return 0.
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